Individualized Risk Prediction for Improved Chronic Wound Management

Veličković, Vladica M. and Spelman, Tim and Clark, Michael and Probst, Sebastian and Armstrong, David G. and Steyerberg, Ewout (2023) Individualized Risk Prediction for Improved Chronic Wound Management. Advances in Wound Care, 12 (7). pp. 387-398. ISSN 2162-1918

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Significance: Chronic wounds are associated with significant morbidity, marked loss of quality of life, and considerable economic burden. Evidence-based risk prediction to guide improved wound prevention and treatment is limited by the complexity in their etiology, clinical underreporting, and a lack of studies using large high-quality datasets.

Recent Advancements: The objective of this review is to summarize key components and challenges in the development of personalized risk prediction tools for both prevention and management of chronic wounds, while highlighting several innovations in the development of better risk stratification.

Critical Issues: Regression-based risk prediction approaches remain important for assessment of prognosis and risk stratification in chronic wound management. Advances in statistical computing have boosted the development of several promising machine learning (ML) and other semiautomated classification tools. These methods may be better placed to handle large number of wound healing risk factors from large datasets, potentially resulting in better risk prediction when combined with conventional methods and clinical experience and expertise.

Future Directions: Where the number of predictors is large and heterogenous, the correlations between various risk factors complex, and very large data sets are available, ML may prove a powerful adjuvant for risk stratifying patients predisposed to chronic wounds. Conventional regression-based approaches remain important, particularly where the number of predictors is relatively small. Translating estimated risk derived from ML algorithms into practical prediction tools for use in clinical practice remains challenging.

Item Type: Article
Identification Number:
7 September 2023Accepted
7 September 2023Published Online
Uncontrolled Keywords: chronic wounds, risk prediction, risk stratification, wound management, personalized therapy
Subjects: CAH02 - subjects allied to medicine > CAH02-06 - allied health > CAH02-06-01 - health sciences (non-specific)
Divisions: Faculty of Health, Education and Life Sciences > School of Health Sciences
Depositing User: Gemma Tonks
Date Deposited: 17 Oct 2023 12:57
Last Modified: 17 Oct 2023 12:57

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